DBMS > EsgynDB vs. Greenplum vs. JaguarDB vs. Kyligence Enterprise vs. STSdb

System Properties Comparison EsgynDB vs. Greenplum vs. JaguarDB vs. Kyligence Enterprise vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Performant, highly scalable DBMS for AI and IoT applications

A distributed analytics engine for big data, built on top of Apache Kylin

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	0.46
Rank	#266	Overall
	#124	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

www.esgyn.cn

greenplum.org

www.jaguardb.com

kyligence.io/kyligence-enterprise

github.com/STSSoft/STSdb4

Technical documentation

docs.greenplum.org

www.jaguardb.com/support.html

Developer

Esgyn

Pivotal Software Inc.

DataJaguar, Inc.

Kyligence, Inc.

STS Soft SC

Initial release

2015

2005

2015

2016

2011

Current release

7.0.0, September 2023

3.3 July 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL V3.0

commercial

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

C++

the server part. Clients available in other languages

Java

Server operating systems

Linux

Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

since Version 4.2

Secondary indexes

yes

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C
Java
Perl
Python
R

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C#
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Source-replica replication

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

rights management via user accounts

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More resources

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